Nada Basit headshot

Nada Basit

Assistant Professor
Unit: School of Engineering and Applied Science
Department: Department of Computer Science
Office location and address
Rice 405
85 Engineers Way
Charlottesville, Virginia 22903
B.S. ​Mary Washington College (now University of Mary Washington), 2003
M.S. ​George Mason University, 2006
Ph.D. ​George Mason University, 2012

Nada Basit is a full-time Assistant Professor in the Computer Science Department at the University of Virginia. She received her PhD in Computer Science from George Mason University and earned her MS degree at GMU as well. She received her BS in computer science from University of Mary Washington. In addition, she has a Graduate Certificate in Biometrics from the Volgenau School of Engineering at George Mason University (2010). While a graduate student at George Mason University, she had extensive teaching experience both as a Graduate Teaching assistant to a number of graduate level courses there, and as an Adjunct faculty member teaching a number of undergraduate courses at University of Mary Washington. She was also selected to be a Research Fellow in the summer of 2001 at the Pratt School of Engineering at Duke University.

CS 1501: Special Topics in Computer Science
Credits: 1
Student led special topic courses which vary by semester.
CS 2100: Data Structures and Algorithms 1
Credits: 4
A second course in computing with an emphasis on foundational data structures and program analysis. The course provides a introduction to object oriented programming and the Java programming language, concurrency, and inheritance / polymorphism. Additionally, foundational data structures and related algorithms / analysis are studied. These include lists, stacks, queues, trees, hash tables, and priority queues. Prereq: CS 1100 - CS 1199
CS 2110: Software Development Methods
Credits: 3
A second course in computing with an emphasis on modern software development and principles central to computer science. Topics include software requirements, testing, object-oriented design, abstraction, encapsulation, recursion, and time-complexity. Prerequisite: CS 1110, 1111, 1112, or 1120 with a grade of C- or higher.
CS 4750: Database Systems
Credits: 3
Introduces the fundamental concepts for design and development of database systems. Emphasizes relational data model and conceptual schema design using ER model, practical issues in commercial database systems, database design using functional dependencies, and other data models. Develops a working relational database for a realistic application. Prerequisite: CS 2150 or CS 2501 topic DSA2 with a grade of C- or higher.
CS 4980: Capstone Research
Credits: 1–3
This course is one option in the CS fourth-year thesis track. Students will seek out a faculty member as an advisor, and do an independent project with said advisor. Instructors can give the 3 credits across multiple semesters, if desired. This course is designed for students who are doing research, and want to use that research for their senior thesis. Note that this track could also be an implementation project, including a group-based project. Prerequisite: CS 2150 with a grade of C- or higher
CS 4993: Independent Study
Credits: 1–3
In-depth study of a computer science or computer engineering problem by an individual student in close consultation with departmental faculty. The study is often either a thorough analysis of an abstract computer science problem or the design, implementation, and analysis of a computer system (software or hardware). Prerequisite: Instructor permission.
CS 4998: Distinguished BA Majors Research
Credits: 3
Required for Distinguished Majors completing the Bachelor of Arts degree in the College of Arts and Sciences. An introduction to computer science research and the writing of a Distinguished Majors thesis. Prerequisites: CS 2150 or CS 2501 topic DSA2 with a grade of C- or higher, and BSCS major
CS 5010: Programming and Systems for Data Science
Credits: 3
The objective of this course is to introduce basic data analysis techniques including data analysis at scale, in the context of real-world domains such as bioinformatics, public health, marketing, security, etc. For the purpose of facilitating data manipulation and analysis, students will be introduced to essential programming techniques in Python, an increasingly prominent language for data science and "big data" manipulation. Prerequisite: CS 1110, Math 1310 or APMA 1110, Math 3351 or APMA 3080, Math 3100, APMA 3010 or APMA 3110
CS 5012: Foundations of Computer Science
Credits: 3
Provide a foundation in discrete mathematics, data structures, algorithmic design and implementation, computational complexity, parallel computing, and data integrity and consistency for non-CS, non-CpE students. Case studies and exercises will be drawn from real-world examples (e.g., bioinformatics, public health, marketing, and security). Prerequisite: CS 5010, CS 1110 or equivalent, Math 1210 or equiv, Math 3351 or equiv, Math 3100 or equiv.
DS 5100: Programming for Data Science
Credits: 3
An introduction to essential programming concepts, structures, and techniques. Students will gain confidence in not only reading code, but learning what it means to write good quality code. Additionally, essential and complementary topics are taught, such as testing and debugging, exception handling, and an introduction to visualization. This course is project based, consisting of a semester project and final project presentations.
CS 6316: Machine Learning
Credits: 3
This is a graduate-level machine learning course. Machine Learning is concerned with computer programs that automatically improve their performance through experience. This course covers introductory topics about the theory and practical algorithms for machine learning from a variety of perspectives. Topics include supervised learning, unsupervised learning and learning theory. Prerequisite: Calculus, Basic linear algebra, Basic Probability and Basic Algorithm. Statistics is recommended. Students should already have good programming skills.
CS 6750: Database Systems
Credits: 3
Studies new database systems, emphasizing database design and related system issues. Explores advanced topics such as object-oriented and real-time database systems, data warehousing, data mining, and workflow. Makes use of either commercial or research database systems for in-class projects. Prerequisite: CS 4750 or equivalent.
CS 6890: Industrial Applications
Credits: 1
A graduate student returning from Curricular Practical Training can use this course to claim one credit hour of academic credit after successfully reporting, orally and in writing, a summary of the CPT experience to his/her academic advisor.
CS 6993: Independent Study
Credits: 1–12
Detailed study of graduate course material on an independent basis under the guidance of a faculty member.
DS 6999: Independent Study
Credits: 1–12
Graduate-level independent study conducted under the supervision of a specific instructor(s)
CS 7993: Independent Study
Credits: 1–12
Detailed study of graduate course material on an independent basis under the guidance of a faculty member.
CS 7995: Supervised Project Research
Credits: 3
Formal record of student commitment to project research for the Master of Computer Science degree under the guidance of a faculty advisor.
CS 8897: Graduate Teaching Instruction
Credits: 1–12
For master's students who are teaching assistants.
CS 8999: Thesis
Credits: 1–12
Formal record of student commitment to thesis research for the Master of Science degree under the guidance of a faculty advisor. May be repeated as necessary.
CS 9897: Graduate Teaching Instruction
Credits: 1–12
For doctoral students who are teaching assistants.